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Electronics
Special Issues
Indoor Localization Using Wireless Sensor Networks
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Indoor Localization Using Wireless Sensor Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 7178

Special Issue Editor

Dr. Jung Min Pak

E-Mail Website
Guest Editor
Department of AI Data Engineering, Korea National University of Transportation, Uiwang-si 16106, Republic of Korea
Interests: state estimation; localization; target tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor localization systems using wireless sensor networks (WSNs) have been used for a variety of industrial purposes, such as tracking workers and equipment in construction sites, vehicle tracking in parking lots, human localization in hospitals or intelligent buildings, cargo tracking systems in logistics, robot tracking in museums or in factories, etc. WSNs for indoor localization utilize various wireless communication technologies, such as radiofrequency identification (RFID), ultrawide band (UWB), and chirp spread spectrum (CSS). Indoor localization algorithms estimate positions of targets using wireless measurements, such as time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS). Since the measurements contain noises, stochastic filters (also known as state estimators) that can reduce the bad effects of measurement noise are often used in localization algorithms. Various stochastic filters, including the Kalman filter, particle filter, and finite impulse response (FIR) filter, have been studied and successfully applied to indoor localization.

This Special Issue focuses on the design, analysis, and implementation of indoor localization systems using WSNs. The topics of interest include but are not limited to:

  • Implementation of indoor localization system using WSNs;
  • Indoor localization algorithm using WSNs;
  • Signal processing for indoor localization using WSNs;
  • Stochastic filters (state estimators) for indoor localization;
  • Analysis of indoor localization technologies using WSNs.

Prof. Dr. Jung Min Pak
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

17 pages, 4447 KiB  
Article
Indoor 3D Localization Scheme Based on BLE Signal Fingerprinting and 1D Convolutional Neural Network
by Shangyi Yang, Chao Sun and Youngok Kim
Electronics 2021, 10(15), 1758; https://doi.org/10.3390/electronics10151758 - 22 Jul 2021
Cited by 15 | Viewed by 3904
Abstract
Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend [...] Read more.
Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm. Full article
(This article belongs to the Special Issue Indoor Localization Using Wireless Sensor Networks)
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10 pages, 435 KiB  
Article
Switching Extended Kalman Filter Bank for Indoor Localization Using Wireless Sensor Networks
by Jung Min Pak
Electronics 2021, 10(6), 718; https://doi.org/10.3390/electronics10060718 - 18 Mar 2021
Cited by 13 | Viewed by 2399
Abstract
This paper presents a new filtering algorithm, switching extended Kalman filter bank (SEKFB), for indoor localization using wireless sensor networks. SEKFB overcomes the problem of uncertain process-noise covariance that arises when using the constant-velocity motion model for indoor localization. In the SEKFB algorithm, [...] Read more.
This paper presents a new filtering algorithm, switching extended Kalman filter bank (SEKFB), for indoor localization using wireless sensor networks. SEKFB overcomes the problem of uncertain process-noise covariance that arises when using the constant-velocity motion model for indoor localization. In the SEKFB algorithm, several extended Kalman filters (EKFs) run in parallel using a set of covariance hypotheses, and the most probable output obtained from the EKFs is selected using Mahalanobis distance evaluation. Simulations demonstrated that the SEKFB can provide accurate and reliable localization without the careful selection of process-noise covariance. Full article
(This article belongs to the Special Issue Indoor Localization Using Wireless Sensor Networks)
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